Renewable Energy Transition for the Himalayan Countries Nepal and Bhutan: Pathways Towards Reliable, Affordable and Sustainable Energy for All

The Himalayan countries Nepal and Bhutan have been confronted with similar climate change and energy emergencies for quite a long time. Its influence is felt as a barrier in financial, social, infrastructural, and political development. Despite having an enormous amount of renewable energy sources, these countries are unable to fulfil their current energy demand. While the power sector is entirely dependent on hydropower, other sectors depend on fossil fuel imports from India. This study offers a pathway for energy independency, energy for all and transition towards a 100% renewables based energy system. The modelling of the energy sector is done using the LUT Energy System Transition model for a period from 2015 to 2050 in a 5-year time step. This study covers the main energy sectors: power, heat, and transport. Two scenarios are visualised, one considering greenhouse gases (GHG) emissions and the associated mitigation cost and another without these costs, though both scenarios aim at achieving a high share of renewable energy by 2050. A substantial drop in levelised cost of energy is observed for the scenario without GHG emission cost, however, taxing GHG emissions will accelerate the energy transition with the levelised cost of energy on a similar level. It is well possible to transition from 90 €/MWh in 2015 to 49 €/MWh by 2050 for the entire energy system by utilizing indigenous low-cost renewable energy. Solar photovoltaics and hydropower will play a dominant role in 2050, having a share of 67% and 31% respectively. Consequently, this leads to zero GHG emissions. An energy transition towards a sustainable and secure energy system for all by 2050 is well possible in Nepal and Bhutan only through 100% renewable sources and it is both technically and economically feasible despite having substantial limitations in infrastructure and economic development currently.

View this article on IEEE Xplore


Energy Optimization in Massive MIMO UAV-Aided MEC-Enabled Vehicular Networks

This paper presents a novel unmanned aerial vehicle (UAV)-aided mobile edge computing (MEC) architecture for vehicular networks. It is considered that the vehicles should complete latency-critical computation-intensive tasks either locally with on-board computation units or by offloading part of their tasks to road side units (RSUs) with collocated MEC servers. In this direction, a hovering UAV can serve as an aerial RSU (ARSU) for task processing or act as an aerial relay and further offload the computation tasks to a ground RSU (GRSU). To significantly reduce the delay during data offloading and downloading, this architecture relies on the benefits of line-of-sight (LoS) massive multiple-input–multiple-output (MIMO). Therefore, it is considered that the vehicles, the ARSU, and the GRSU employ large-scale antennas. A three-dimensional (3-D) geometrical representation of the MEC-enabled network is introduced and an optimization method is proposed that minimizes the computation-based and communication-based weighted total energy consumption (WTEC) of vehicles and ARSU subject to transmit power allocation, task allocation, and time slot scheduling. The results verify the theoretical derivations, emphasize on the effectiveness of the LoS massive MIMO transmission, and provide useful engineering insights.

*Published in the IEEE Vehicular Technology Society Section within IEEE Access.

View this article on IEEE Xplore


An Optimal Home Energy Management Paradigm With an Adaptive Neuro-Fuzzy Regulation


In the smart grid paradigm, residential consumers should participate actively in the energy exchange mechanisms by adjusting their consumption and generation. To this end, a proper home energy management system (HEMS), in addition to achieving a high level of comfort for the consumers, should handle the practical difficulties due to the uncertainty and technical limits. With this aim, in this paper, a new HEMS is proposed to carry out day-ahead management and real-time regulation. While an optimal scheduling solution based on some forecasted values of uncertain parameters is achieved for day ahead management, real-time regulation is accomplished by an adaptive neuro-fuzzy inference system, which can regulate the gaps between the forecasted and real values. Investigated case studies indicate that the proposed HEMS can find an optimal operating scenario with an acceptable success rate for real-time regulation.

View this article on IEEE Xplore